Big Data Success: 3 Companies Share Secrets

Three C-level execs say start with a clear business goal, consider the data and finish with human-understandable analytics.

Start with a focused, business-driven project, make sure the data is consistent with your vision and then apply advanced analytics without moving beyond human-understandable decisions.

These were three consistent themes that emerged this week during a big data panel discussion at Interop New York.

"Lots of companies are saying 'let's invest in big data,' but we started with the question, 'what's the use case?'," said panelist Gary Hoberman, global business CIO at MetLife. "Big data happened to be the answer to solve the problem of knowing customers better."

MetLife's answer was a NoSQL-driven MetLife Wall project that captures a 360-degree view of customers by consolidating key customer information from more than 70 internal systems in MongoDB. Data on customers is rendered in a Facebook Wall-like intranet view that has simplified claims research and customer service interactions.

British Airways (BA) is another big data practitioner that wanted to improve customer centricity. Where the airline's knowledge of customers was previously siloed across operational systems and the loyalty program, a Know Me program instituted last year blended loyalty information with data on the online behavior and buying habits of 20 million BA customers.

Know Me is powered in part by analytics software from Opera Solutions that helps the airline come up with targeted thank you offers for continued loyalty or upgrade offers to offset service lapses such as misplaced luggage.

"Previously it took six to nine months to build a single analytical application at BA, but now they can do it in two to three weeks," said panelist Arnab Gupta, CEO of Opera Solutions. "That has let loose their innovative energy, and if they want to come up with a seating upgrade offer, the can quickly figure out who to target."

Opera works across many industries, and Gupta said the most consistent big data stumbling block he encounters is the mindset that it has to be a big-bang IT infrastructure project. "People make the problem larger than it needs to be," he said. "If you just shift your perspective to solving specific business problems in specific domains, it will open up a world of projects you can get done relatively quickly."

Not many companies mix such a variety of data, but data-enrichment is a common practice in digital marketing and customer-segmentation initiatives. What's TRA's advice for working with third-party data, whether that's commercial data or public data sources? Mark Lieberman, the chairman and co-founder of TRA and the third Interop panelist, advised practitioners to look for at least some commonality in data sources.

Most IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.

Why should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.